Though a considerable number of publications about biodiesel can be found in literature, several problems remain unsolved, encompassing economical, social, and technical issues. Thus, the biodiesel industry has come under attack by some environmental associations, and subsidies for biofuel production have been condemned by some governments. Yet, biodiesel may represent a truly competitive alternative to diesel fuel, for which fuel tax exemption and subsidies to energetic crops are needed. Biodiesel must increase its popularity among social movements and governments to constitute a valid alternative of energy source. In this sense, the use of nonedible oils to produce biodiesel is proposed in the present review. Moreover, the compromise of noninterference between land for energetic and food purposes must be addressed. Concerning technical issues, it is important to consider a transesterification optimization, which is missing or incomplete for too many vegetable oils already tested. In most cases, a common recipe to produce biodiesel from any raw material has been adopted, which may not represent the best approach. Such strategy may fit multifeedstock biodiesel plant needs but cannot be accepted for oils converted individually into biodiesel, because biodiesel yield will most likely fail, increasing costs. Transesterification optimization results depend on the chemical composition of vegetable oils and fats. Considering "sustainable" vegetable oils, biodiesel from Calophyllum inophyllum, Azadirachta indica, Terminalia catappa, Madhuca indica, Pongamia pinnata, and Jatropha curcas oils fits both current biodiesel standards: European EN 14214 and US ASTM D 6751 02. However, none of them can be considered to be the "ideal" alternative that matches all the main important fuel properties that ensure the best diesel engine behavior. In search of the ideal biodiesel composition, high presence of monounsaturated fatty acids (as oleic and palmitoleic acids), reduced presence of polyunsaturated acids, and controlled saturated acids content are recommended. In this sense, C18:1 and C16:1 are the best-fitting acids in terms of oxidative stability and cold weather behavior, among many other properties. Furthermore, genetic engineering is an invaluable tool to design oils presenting the most suitable fatty acid profile to provide high quality biodiesel. Finally, most published research related to engine performance and emissions fails in using a standard methodology, which should be implemented to allow the comparison between tests and biofuels from different origin. In conclusion, a compromise between social, economical, and technical agents must be reached.

Here, we use a closed-loop discovery and optimization approach for searching the peptide sequence space. Combining an evolutionary algorithm with machine learning and in vitro assay allowed for rapid development of new antimicrobial peptides.

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Sweat is one of the less employed biofluids for discovery of markers in spite of its increased application in medicine for detection of drugs or for diagnostic of cystic fibrosis. In this research, human sweat was used as clinical sample to develop a screening tool for lung cancer, which is the carcinogenic disease with the highest mortality rate owing to the advanced stage at which it is usually detected. In this context, a method based on the metabolite analysis of sweat to discriminate between patients with lung cancer versus smokers as control individuals is proposed. The capability of the metabolites identified in sweat to discriminate between both groups of individuals was studied and, among them, a trisaccharide phosphate presented the best independent performance in terms of the specificity/sensitivity pair (80 and 72.7%, respectively). Additionally, two panels of metabolites were configured using the PanelomiX tool as an attempt to reduce false negatives (at least 80% specificity) and false positives (at least 80% sensitivity). The first panel (80% specificity and 69% sensitivity) was composed by suberic acid, a tetrahexose, and a trihexose, while the second panel (69% specificity and 80% sensitivity) included nonanedioic acid, a trihexose, and the monoglyceride MG(22:2). Thus, the combination of the five metabolites led to a single panel providing 80% specificity and 79% sensitivity, reducing the false positive and negative rates to almost 20%. The method was validated by estimation of within-day and between-days variability of the quantitative analysis of the five metabolites

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